The explosion of information available over network-based systems, such as the internet can overwhelm a person attempting to locate a desired piece of information or product. For example, over the last decade the categories of products available through a typical network-based commerce system has grown exponentially. This dramatic growth has left users with the problem of sorting and browsing through enormous amounts of data to find information or products relevant to their needs. Recommendation systems have been implemented to attempt to assist users in locating relevant information or products. A successful recommendation system on a network-based publishing or commerce site not only saves users time in locating relevant information (e.g., products) but can also drive extra profits through advertising or additional sales revenue.
Most current recommendation systems use some form of collaborative filtering to produce a single scalar number for each potential relationship. Two different basic types of collaborative filtering are typically employed by recommendation systems, user-based or item-based. User-based collaborative filtering focuses on grouping like user behavior. Item-based recommendation systems focus on grouping similar items. Item-based recommendations using collaborative filtering is used by commerce sites to provide recommendations based on the purchase history of users that bought similar products (e.g., users who brought X also brought Y and Z).